Bulk Loss Run Extraction for Reinsurance Due Diligence: AI-Driven Risk Assessment at Portfolio Scale

Reinsurance risk analysts are asked to do the near-impossible on tight timelines: digest hundreds of heterogeneous Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC) filings, and Claim Register Exports to form a fast, defensible view of historical loss performance and future tail risk. Formats vary by cedent, line of business, TPA, and year. Field names conflict; attachments, deductibles, and ALAE/ULAE are labeled differently; reopens and restatements cascade through time. Meanwhile, renewal clocks don’t slow down.

Doc Chat by Nomad Data turns that mountain of documentation into a portfolio-scale advantage. Purpose-built for insurance documents, Doc Chat’s AI agents ingest entire submission packages, automate loss run extraction and normalization, and let analysts ask real-time questions across thousands of pages: “Show all Workers’ Compensation reopens in AY 2017 with Paid ALAE > $50K,” or “Reconcile ceded paid vs. incurred by AY to Schedule F.” The results are structured, cited back to source pages, and ready for pricing models and portfolio analytics within minutes.

The Reinsurance Due Diligence Challenge for a Reinsurance Risk Analyst

Reinsurance due diligence is not just document collection; it’s a high-stakes exercise in truth-finding across inconsistent, multi-source data. A Reinsurance Risk Analyst must translate hundreds of cedent-specific loss formats into a coherent, comparable dataset that supports treaty pricing, portfolio optimization, and capital allocation. The nuances differ by line and by cedent:

For casualty books (e.g., General Liability, Auto Liability, Products, Professional Liability), tail risk hides in long development patterns, volatile ALAE, litigation-heavy jurisdictions, and uneven reserving philosophies. For Workers’ Compensation, reopens and medical cost inflation complicate loss development; cause-of-injury and body-part coding may shift across time or vendors. In Property, catastrophe event tagging can be missing or partial, accruals can be embedded with partial recoveries, and negative transactions create confusing run-rate signals. In Accident & Health or Specialty lines, bordereau detail can vary monthly and introduce data drift that isn’t obvious in a static snapshot.

On top of this, cedents often supply a mixture of Loss Run Reports (PDF and Excel), monthly cedent loss bordereaux (XLS/CSV), Claim Register Exports (from core admin or TPA systems), and statutory context from Schedule F (NAIC). The analyst’s job is to normalize claim-level and aggregate fields, reconcile paid/incurred triangles, isolate reinsurance layer impacts, and ensure that experience rating inputs reflect apples-to-apples treatment of ALAE, salvage/subro, deductibles, SIRs, and attachments. This is precisely where manual processes bend and often break.

How the Process Is Handled Manually Today

Most teams still tackle cedent submissions with spreadsheets, VLOOKUPs, and heroic domain knowledge. A typical renewal or portfolio acquisition diligence might unfold like this:

Submission arrives via secure email or portal with a zip of PDFs, Excel files, CSVs, and scanned reports. Analysts first triage document types: multi-tab Loss Run Reports, monthly bordereaux, Claim Register Exports with varied column headers, and the cedent’s latest Schedule F (NAIC) to anchor ceded balances and recoverables. They then create a temporary mapping dictionary (“Paid LAE” vs. “Paid ALAE” vs. “Expense Paid”), build macros to standardize date formats, and hand-reclassify cause-of-loss codes, LOB tags, and jurisdiction fields to match the reinsurer’s canonical schema.

Advanced tasks follow: deduplicating claim IDs that change across policy periods, reversing negative entries tied to corrections, isolating ALAE from indemnity in incurred, recreating triangles by AY and RY, and reconciling ceded balances against Schedule F exhibits. Analysts compile pivot tables to check reasonableness of development patterns, search for missing accident dates, and call out unusual reopens, large loss step-changes, and late-reporting spikes that may distort LDF selection. In parallel, pricing teams are waiting for clean inputs to run rate indications, swing scenarios, and tail factor sensitivity.

Even for seasoned professionals, manual diligence is a slog. Hundreds of hours disappear into formatting, re-keying, and reconciliation. Quality varies across reviewers and time constraints. The inevitable consequence: slow cycle time, inconsistent normalization, and potential blind spots that materially affect treaty pricing.

Why Loss Run Extraction and Normalization Are Inherently Hard

Loss runs are not standardized, and the information you need is rarely located in predictable cells. The analyst must infer meaning: Is “Expense” inclusive of ALAE only, or does it include ULAE? Does “Incurred” reflect case or case+IBNR? Was a claim merged, and if so, where is the audit trail? Are negative payments corrections or subrogation recoveries? Did the cedent change TPAs, bringing new naming conventions and idiosyncrasies?

This is the difference between extraction and understanding. As Nomad explains in its article “Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs”, the work requires inference across variable structures, connecting breadcrumbs scattered throughout thousands of pages and encoding institutional knowledge that is rarely written down. Manual teams can do this, but not at portfolio scale and not consistently under renewal pressure.

AI to extract claims from loss runs for reinsurance: what great looks like

For an AI system to be genuinely useful to a Reinsurance Risk Analyst, it must do more than OCR. It must classify document types, normalize semantics across cedents, validate against independent anchors (like Schedule F (NAIC)), and expose a real-time Q&A interface with page-level citations. This is precisely what Doc Chat delivers, and why carriers like GAIG see dramatic cycle-time and quality gains in complex-document workflows (see Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI).

How Nomad Data's Doc Chat Automates Bulk Loss Run Data Digitization for Portfolio Review

Doc Chat is a suite of insurance-trained, AI-powered agents that ingest entire claim files and submission bundles at once, extracting and normalizing every relevant field into your canonical data model. It is built for the messy realities of reinsurance due diligence and renewal workflows:

• High-volume ingestion: Upload zips containing PDFs, Excels, and CSVs across dozens or hundreds of cedents. Doc Chat classifies each file as Loss Run Report, Cedent Loss Bordereau, Claim Register Export, or Schedule F, then routes to the correct parsing and mapping pipeline.
• Document understanding with inference: The system interprets inconsistent headers (Paid LAE vs. Paid ALAE), alternate date formats, jurisdiction shorthands, and custom cause-of-loss schemes, using your playbook to determine canonical fields and business rules.
• Normalization and mapping: All claims are standardized into a structured output: claim_id, policy_number, LOB/sub-LOB, accident_date, report_date, jurisdiction, paid_indemnity, paid_ALAE, incurred_indemnity, incurred_ALAE, case_reserve splits, status, reopen flag, closure date, salvage/subro, deductible/SIR, attachment points, layer participation, and more.
• Reconciliation and validation: Automatically reconcile ceded paid vs. incurred by accident year to Schedule F (NAIC) recoverables and ceded balances. Flag mismatches, negative patterns, unusual reopens, and late reporting spikes for human review.
• Real-time Q&A: Ask questions like “List all AY 2016 GL claims with development > 50% in the last 12 months” or “Show all WC medical-only claims that re-opened after 24 months,” and get structured answers with citations linking back to source pages.
• Output-ready for pricing: Export claim-level tables and pre-built triangles by AY/RY for experience rating. Push clean datasets to Excel, CSV, Snowflake, S3, or pricing workbooks via API.

This is not theoretical. Nomad Data routinely sees submissions that previously required days of manual effort turned around in minutes, with higher consistency and instant auditability. For more on why automating “data entry” at scale drives outsized ROI, see Nomad’s article AI's Untapped Goldmine: Automating Data Entry.

Normalize ceded loss data with AI: what Doc Chat extracts out-of-the-box

Doc Chat standardizes the details that matter to reinsurance risk and pricing decisions. A typical extraction/normalization pass can return a comprehensive, clean dataset that includes:

• Core claim identity: claim_id, master claim id (when merged), policy_number, policy_year, insured_name, insured_id, cedent id.
• Timing: accident_date, report_date, close_date, reopen_flag and reopen_date, valuation_date, transaction dates.
• Coverage and LOB tagging: NAIC line, sub-line (e.g., GL-products vs. premises), occurrence/claims-made indicator, retro dates, per-occurrence/aggregate attachments and limits, participation percentage.
• Jurisdiction and venue: state, country, court venue (if noted), compensability codes (for WC), litigation status.
• Financials: paid_indemnity, paid_ALAE, paid_total, incurred_indemnity, incurred_ALAE, case reserves (indemnity/expense), recoveries (salvage/subro), deductible/SIR used, outstanding balances.
• Cause and severity: cause_of_loss, body_part (WC), injury_nature, severity flags (cat threshold, large loss tags), event codes if provided.
• Reinsurance alignment: ceded vs. gross flags, layer mapping, occurrence or aggregate exhaustion indicators, ceded shares by layer, clash/cat markers where applicable.
• Quality checks: negative payment detection, out-of-sequence transactions, late reporting anomalies, inconsistent ALAE handling, shifts in TPA coding practices.

Because Doc Chat is trained on your playbooks and standards, the mapping aligns to your canonical schema and pricing templates. Output is consistent whether the input is a clean Excel bordereau or a scanned Loss Run Report PDF with mixed fonts.

Automated loss bordereaux analysis reinsurance: portfolio-scale insight in minutes

Once Doc Chat has normalized loss runs and bordereaux, it can produce ready-to-use analytics for a Reinsurance Risk Analyst:

Experience triangles by AY and RY, with split views (indemnity vs. ALAE), are generated automatically. Outliers such as step-changes in case reserving, abnormal reopen rates by jurisdiction, and late-reported large losses are highlighted for targeted review. For Property, Doc Chat can cluster events by date/location to identify likely catastrophe groupings, even when explicit event IDs are missing in the cedent files.

The agent can also run automated reasonableness checks: “Does ceded paid by AY reconcile within tolerance to Schedule F (NAIC) recoverables?” or “Did the cedent change ALAE accounting between 2021 and 2022?” Any flagged exceptions are pushed to a human for adjudication, with citations pointing to the exact lines and pages driving the discrepancy.

How the Process Feels With Doc Chat in the Loop

Analysts typically begin by dragging a submission zip into Doc Chat’s interface or staging it into the API endpoint. The system classifies, loads, and processes files at enterprise scale, building a structured dataset within minutes. From there:

You can ask natural-language questions across the entire submission: “Show incurred development over the last 24 months for AY 2015 WC in California,” “List reopens > 12 months after close,” “Find claims that crossed attachment in the last valuation.” Every answer is returned with page-level references back to original Loss Run Reports, Claim Register Exports, or cedent loss bordereaux. If something looks off, click through, confirm, and annotate. The dataset can then be exported to your pricing workbook or analytics environment. No re-keying, no email ping-pong, no guessing.

This mirrors the transformation documented in Nomad’s client story: GAIG’s complex claim reviews that took days now complete in moments. Complex due diligence benefits from the same engine: fast ingestion, precise answers, and source-linked transparency.

Business Impact: Time, Cost, Accuracy, and Better Negotiation

When you eliminate manual normalization, the economics shift immediately. Consider a mid-sized renewal with 85 cedents and roughly 400 files (mixed Loss Run Reports, monthly bordereaux, and Claim Register Exports). Traditional review might consume 300–600 analyst hours just to deliver a clean dataset. With Doc Chat, that becomes minutes to hours, not weeks.

Quantifiable impact for a Reinsurance Risk Analyst and their organization includes:

• Time savings: Reviews move from days to minutes, removing bottlenecks at the busiest point in the renewal calendar.
• Cost reduction: Less overtime, fewer external consultants for data cleanup, and the ability to scale without adding headcount.
• Accuracy and consistency: Doc Chat applies the same rules, every time, across every cedent. It never “skips” a page. Page-level citations ensure defensibility.
• Stronger negotiation: With normalized facts in hand, you identify adverse development sooner, challenge anomalies confidently, and tailor terms with precision.
• Portfolio transparency: You can compare cedents apples-to-apples and spot concentration, tail risk, or reserving shifts across time and lines.

Beyond efficiency, the quality of insight improves. The engine surfaces reopens, late-reporting spikes, and ALAE handling changes that humans routinely miss under deadline. As outlined in Nomad’s piece The End of Medical File Review Bottlenecks, the machine reads page 1,500 with the same attention as page 1—and that consistency is transformative when building a defensible view of tail risk.

Why Nomad Data Is the Best Partner for Reinsurance Document Automation

Most providers offer generic OCR or templated extraction. Reinsurance needs more. Nomad brings three differentiators that matter in due diligence and pricing:

1) Built for complexity, not just volume. Reinsurance books include everything from clean Excel to scanned PDF loss runs with handwriting. Doc Chat is trained on insurance semantics, so it can dig out exclusions, endorsements, attachment points, and expense handling buried deep in inconsistent content. It surfaces every reference to coverage, liability, or damages signals and eliminates blind spots that lead to leakage.

2) The Nomad Process—white glove, playbook-driven. We train Doc Chat on your playbooks, canonical fields, and normalization rules so the output fits like a glove. Our team interviews your analysts, captures “the rules that don’t exist,” and encodes them into AI workflows—exactly as described in Beyond Extraction. This hybrid discipline is our core competency.

3) Fast, low-lift implementation (1–2 weeks). Start with drag-and-drop uploads on day one. As you scale, our modern APIs integrate with your pricing spreadsheets, Snowflake, or data lake. Typical initial deployments move from kickoff to production in one to two weeks—no need to wait months to see value.

Security and governance are first-class. Nomad maintains SOC 2 Type 2 controls. Every answer includes verifiable citations, satisfying auditors, reinsurers, and regulators who demand transparent lineage.

From Raw Documents to Pricing-Ready Data: A Closer Look at the Automation Flow

To illuminate how Doc Chat delivers results for a Reinsurance Risk Analyst, consider the end-to-end flow on a typical renewal:

1) Intake & classification. Upload cedent packages (zip of PDFs/Excels/CSVs). Doc Chat identifies Loss Run Reports, Cedent Loss Bordereaux, Claim Register Exports, and Schedule F (NAIC), then routes each to specialized AI agents.

2) Parsing & inference. Agents extract tabular data and narrative context. They infer semantics around ALAE vs. ULAE, case vs. case+IBNR, and reconcile missing or inconsistent fields per your playbook.

3) Normalization & mapping. Data is mapped to your canonical schema—fields standardized, dates formatted, jurisdictions normalized, and LOB/sub-LOB tagging harmonized.

4) Validation & reconciliation. The system reconciles aggregates to Schedule F and flags exceptions (negative paid anomalies, late-report spikes, unusual reopen patterns). Human reviewers approve or annotate exceptions within the interface.

5) Analytics & Q&A. Experience triangles, large-loss lists, and severity/frequency profiles are ready. Analysts ask questions across the unified dataset and click through citations for immediate source verification.

6) Export & integration. Push clean datasets to pricing workbooks, BI dashboards, or data warehouses. Re-run extractions as cedents send updated valuations—Doc Chat tracks versions and highlights changes.

Concrete Scenarios Where Doc Chat Changes the Game

Casualty Treaty Renewal. You receive 60+ cedent submissions with mixed Loss Run Reports and Claim Register Exports. Doc Chat normalizes claim-level severity and ALAE handling across cedents, identifies late-reporting spikes in AY 2018 GL, and flags an outlier TPA whose reserving philosophy shifted in 2022. Pricing now reflects defensible adjustments, and negotiations are tighter and faster.

Property Cat Retro Review. A cedent’s property bordereaux arrive monthly. Doc Chat clusters losses by date/region to infer event groupings, spots negative paid reversals tied to a CAT from two valuation cycles ago, and reconciles ceded totals to Schedule F (NAIC) balances. You see which events are still developing, assess potential tail, and optimize capital deployment.

Runoff Portfolio Acquisition. For M&A diligence on a long-tail casualty book, Doc Chat mines multi-year Loss Run Reports and cedent bordereaux to build AY/RY triangles, isolate reopens beyond 24 months, and compute severities in key jurisdictions. The buyer gains a reliable tail view, validated against statutory anchors, in days—not weeks.

Answer Engine Optimization for Your Team: Make Every Question Actionable

Because Doc Chat supports true, portfolio-wide Q&A, it behaves like an on-demand analyst with perfect memory and instant page-level recall. This matters in reinsurance, where the best questions are iterative:

Start with, “Show AY 2016–2019 WC triangle for indemnity only.” Follow with, “Now split California vs. non-California.” Then, “List reopens older than 18 months that drove adverse development in the last valuation for CA.” Every step is fast, cite-backed, and exportable. As Nomad describes in Reimagining Claims Processing Through AI Transformation, keeping humans in the loop ensures the AI augments expert judgment without replacing it.

Best Practices to Launch Bulk Loss Run Data Digitization for Portfolio Review

Teams that succeed with Doc Chat share a few habits:

Define a canonical schema early. List fields you always need for pricing and portfolio analytics. Doc Chat will target those first and enrich further over time.

Codify the “unwritten rules.” If your analysts know that “Paid Expense” means ALAE for Cedent A but includes ULAE for Cedent B, turn that into a playbook rule. We’ll encode it.

Start with a representative sample, then scale. Use 5–10 typical cedents to calibrate mappings. Once validations match your expectations, scale to the full portfolio.

Insist on page-level explainability. When anomalies arise, jump straight to the exact page and line. This preserves trust with underwriting leadership, actuaries, and auditors.

Governance, Security, and Auditability

Reinsurers operate under tight controls. Doc Chat is designed accordingly: SOC 2 Type 2, role-based access, detailed audit trails, and immutable lineage from normalized fields back to document pages. Every transformation is traceable, so your conclusions are not just fast—they’re defensible. As highlighted in the GAIG case study, page-level explainability builds trust across compliance and oversight stakeholders.

Frequently Asked Questions from Reinsurance Risk Analysts

Can Doc Chat reconcile ceded totals to Schedule F (NAIC)? Yes. Doc Chat computes ceded paid/incurred aggregates by AY/RY and checks them against Schedule F recoverables and balances, flagging material deltas for review.

How does it handle negative payments and corrections? The system tracks transaction sequences, identifies reversal patterns, and normalizes net positions per playbook rules. Suspicious sequences are surfaced as anomalies.

Can we get immediate outputs for pricing? Yes. Doc Chat exports normalized claim-level tables and pre-built triangles, ready for your experience rating templates, exposure models, or BI dashboards.

What about mixed formats and scans? Doc Chat was built for this. It handles scans, inconsistent headers, multi-tab Excels, and CSVs—at portfolio scale.

How long does implementation take? Typical deployments go live in 1–2 weeks. You can start with drag-and-drop and layer in API integrations as you scale.

Why Now: The Competitive Advantage of AI in Reinsurance Due Diligence

Workloads will not get lighter. Cedent variability, document volume, and time pressure will continue to climb. The market advantage goes to reinsurers who can convert heterogeneous documentation into normalized insight the same day it’s received. That’s the essence of Doc Chat for Insurance: end-to-end document automation that matches the realities of reinsurance due diligence.

As Nomad argues in AI for Insurance: Real-World AI Use Cases Driving Transformation, early adopters are already reaping the rewards—lower operating costs, faster cycle times, and better decisions. In reinsurance, those gains translate directly into sharper pricing, improved loss ratios, and more resilient portfolios.

Putting It All Together: A Day-in-the-Life with Doc Chat

8:30 a.m.: A cedent uploads a renewal package—50 files including Loss Run Reports, monthly cedent loss bordereaux, a full Claim Register Export, and the latest Schedule F (NAIC) exhibits. You drag-and-drop the zip into Doc Chat.

8:36 a.m.: Classification and parsing complete. A normalization preview shows key mappings, with 11 exceptions flagged (negative paid corrections for AY 2017; a sudden shift in ALAE labeling in 2022; a set of claims missing accident dates).

8:45 a.m.: You accept suggested mappings, annotate two edge cases, and export a pricing-ready dataset. You run initial experience indications and notice adverse development in AY 2015–2016 GL driven by California litigation. You ask Doc Chat for a list of reopens older than 18 months and get a cite-linked table within seconds.

9:10 a.m.: With facts assembled, you brief the underwriter. The negotiation strategy shifts: stricter terms, tighter ALAE treatment, focused collateral discussion for late reporting. By lunch, the team has a defensible position—with source citations—ready for the cedent call.

From Manual to Modern: Make AI Your Reinsurance Multiplier

Manual cleanup is no longer the price of admission for high-quality reinsurance analysis. With Doc Chat, you can scale your AI to extract claims from loss runs for reinsurance, normalize ceded loss data with AI, and drive automated loss bordereaux analysis reinsurance—all within an operating model that honors your playbooks and governance requirements.

If your team is wrestling with bulk loss run data digitization for portfolio review, the fastest path to impact is to start small and scale quickly. Nomad’s white-glove, 1–2-week onboarding makes that easy. Upload a representative set of files, validate outputs together, and then expand to the next renewal wave or M&A opportunity. The sooner you begin, the sooner you convert documentation sprawl into pricing-ready insight.

See Doc Chat for Insurance and turn your reinsurance due diligence into a portfolio-scale advantage.